MODEL-BASED COMPUTATIONAL TOOLS FOR IMAGING, …

MODEL-BASED COMPUTATIONAL TOOLS FOR IMAGING, DIAGNOSTICS AND POD ESTIMATION

Pierre CALMON and coll.

WFNDEC Workshop POD, Imaging, Sizing, Portland July 14th, 2019

| 2WFNDEC 2019 workshop , Portland, July 13th | Pierre CALMON

MODELING &

SIMULATION

INNOVATIVE

METHODSIMAGING &

INSTRUMENTATION

NDE & SHM @ CEA LIST

PARTENARIAL RESEARCH

Focus on Digital technologies

Industrial partnership : Energy, Aircraft Industry railways, oil an gas, manufacturing, …

Strong academic links (CIVAMONT)

NDE & SHM RESEARCH ACTIVITY

Modelling, Simulation, Data: CIVA

Instrumentation, methods

80 permanent people, 20 thesis

Technological transfers to the industry

At the heart of

Campus Paris-Saclay

CEA: French Atomic and Alternative Energy Commission- Public Research Organization

CEA LIST: Institute of the Technological Research Branch of CEA


OUTLINE

A brief look at challenges and trends in modelling: - Physics-based & data driven models

Model-based computational tools for reliability assessment: - MAPOD and Meta-models- State of the art, challenges & new ideas

Model-based computational tools for diagnostics- Model-based (UT) imaging,

- Machine-learning (or iterative inversion) for defect haracterization

A choice: To limit the talk to UT/GW applications.

POD, Imaging, Sizing… using model-based computational tools

ModellingNew trends and potentialities


The objectives of modelling are more or less unchanged :

NDT performance demonstration

Design of inspections, of SHM systems

Imaging and diagnostics: enhanced/automated

Training, monitoring, “virtual NDT”

SIMULATION: CHALLENGES AND APPROACHES

On line tools

But with new uses implying new challenges :

Intensive computations (statistics)

Real time computations for on line applications

+ Increasing complexity, accuracy, realism

And an enlarged vision of “modeling”:

Physics-based models

Data-based models: Meta-models

Off line tools


Need to handle always more complex cases

PHYSICS BASED MODELS (UT)

With the challenges of accuracy/rapidity/easiness of use:

Progress of numerical solutions (FEM)

Complementarity Semi-analytical/numerical

Hybrid models/domain decomposition/multiscale meshing

Implementation of a mathematical formulation of the physical problem

t = 26.2 µs t = 43.7 µs

t = 87.5 µs t = 140 µs

t = 26.2 µs t = 43.7 µs

t = 87.5 µs t = 140 µs

Example CFRP 2mm plate 9 layers 0 -90

Holes

Sensor(100kH)

UT CompositesUT Welds

GW SHM

Cf E. Demaldent’s talk Cf O. Demesnil’s talk



PHYSICS BASED MODELS (UT)

With the challenges of accuracy/rapidity/easiness of use





FEM box

Example of GW : Hybrid model SAFE+FEM



PHYSIC BASED MODELS (UT)

With the challenges of accuracy/rapidity/easiness of use





In progress: Hybrid FEM/FEM for variations studies on defects

Coarse mesh Finer mesh


DATA DRIVEN MODELS (METAMODELS)Construction of a model from (real or numerical) data:

One advantage: Makes possible fast/intensive computations after a phase of building the model.

If based on real data: No needs of physical models (complex/random phenomena)

But one requirement: To have data.

Physics-based models can provide data !


METAMODELS BUILT FROM NUMERICAL DATA

Metamodel: Input/Output “black-box” built from a numerical data base, which can be substituted to the initial physics-based model

"Off line" phase: Possibly time consuming

"On line" phase: Possibly real-time

Exploitation of the metamodel

Data base generation

Creation of the metamodel(Kriging, RBF, SVR, ...)

Cross-validation


METAMODELS BUILT FROM NUNERICAL DATA

Design of Experiment (DoE)

Adaptive design

Pros.: easy to performCons.: non optimized(many samples may havelow information amount)

Pros.: very efficient (sampling Only the informative regions)Cons.: dedicated algorithms

Sparse grid approach (interpolation)

Kernel/mesh -based approach (regression)

Pros.: highly parallelizedCons.: performance decreases with the number of dimensions)

Pros.: can handle moderately highnumber of dimensions Cons.: required an accurate tuningof hyper-parameters

Which kind of sampling strategy?

Which kind of metamodel ?

Kriging Radial basis functionKernel ridge regressionSupport vector machineMultilinear interpolator…

Output Space Filling (OSF)Feature Space Filling (FSF)

S. Ahmed, et al, An adaptive sampling strategy for quasi real

time crack characterization on EC signals, NDT&E Int, 2018


METAMODELS BUILT FROM REAL DATA

D. Rodat, F. Guibert, N. Dominguez, and P. Calmon, ‘Introduction of physical knowledge in kriging-based meta-modelling

approaches applied to Non-Destructive Testing simulations’, Simulation Modelling Practice and Theory, vol. 87, 2018

Data driven models based on real data for the synthesis of:

3D «ultrasonic texture » due to backscattered noise

Ultrasonic response of impact damages (Cscan)

Ultrasonic responses of FBH

Kriging model enriched by Physics

20

20

[mm]

[mm]

0

1.5

20

[mm]

[µs]0

1

0

Am

plit

ude [

a.u

.]

1

-1

Am

plit

ude [

a.u

.]

Real C-scan

20

[mm]

[mm]

0

Simulated C-scan

Real B-scan

1.5

20

[mm]

[µs]0

Simulated B-scan

Real Synthesised Real Synthesised

Phenomenological model basedon the observation of real CscansRealism tested on operators

Reference

[mm]

[mm]

[µs]

Simulation

50

25

- 0.6

0.6

[µs]7

[mm]

[mm]

Adaptation of Markov random field (computer graphics algo.)

From D. Rodat’s PhD, 2018

Here: Objective of « realistic simulation » for Virtual NDE

MODEL-BASED COMPUTATIONAL TOOLS FOR

NDE RELIABILITY ASSESSMENT:

Metamodels and MAPOD


BACKGROUND: RELIABILITY ASSESSMENT AND POD

Depending on the industrial sector/country deterministic (worst case) or probabilistic approaches (POD).

Probabilistic approach: Estimation of POD

NDE reliability assessement : A key challenge

From ENIQ Rep. 41

Threshold

Based on a statistical analysis of laboratory trials: Needs samples & resources

Scattering of the results Probability of detection

Flaw size

Statistical analysis framework in reference

documents: MIL-HDBK-1823A, ENIQ-R41, …


MODEL-BASED RELIABILITY ASSESSMENT

First goal: To replace expensive and sometimes uneasy to implement experimental trials by numerical simulation

Very first POD module in CIVA in 2010

In 2016 publication of a IIW Recommanded practice on MAPOD

Deterministic: Well established acceptance of the use of modelling for Inspection qualification by ENIQ (Nuclear, Europe).

Probabilistic: Active R&D during the last decade on MAPOD, a growing interest in various industries. MAPOD Group driven by USAF at CNDE (2003-2011)

European Project PICASSO (2009-2013) + French national projects projets

In 2010 First POD module in CIVA

MAPOD Group, 2003-2011CNDE, USAF,… MAPOD Group driven by USAF at CNDE (2003-2011)



MAPOD key idea: To introduce variations of the inputted parameters of the model. The variability of the output of the simulation reproduces the scattering of real NDE results.













Statistical

Analysis

Variability POD

Data set


SOME EXAMPLES OF MAPOD STUDIES (CIVA)

[1] N. Dominguez et al, POD Evaluation using simulation: PAUT

case on a complex geometry part, AIP Conf. Proc. 1581, 2031 (2014)

Aircraft Industry: PAUT fatigue cracks in an engine pylon part (2014)[1]

Oil and Gas :Automated UT of pipeline girth welds (2013)[2]

[2] B. Chapuis et al, Simulation supported POD curves for automated UT of pipeline girth welds, Welding in the world, V58, 433-441, (2014)

Part NDT

Geometry: 3D complex shape

Material: Titanium

Defects: Fatigue cracks (specific location)

Configuration: Phased array UT

Contact probe, Sectorial-scanning (-30°;+30°) &

probe motion

Probe: Linear 16 elements, pitch 0.6 mm, 5 MHz

Calibration: Backwall echo

Conditions: Limited access (armhole). The

operator does not see his hand.

a90/95 = 2.96 mm x


SOME EXAMPLES OF MAPOD STUDIES (CIVA)

[1] “Model-based POD study of manual ultrasound

inspection and sensitivity analysis using metamodel”

G. Ribay et al, AIP Conf. Proc. 1706 (2016)

Nuclear Industry: Manual ultrasound inspection of heavy metallic (2015)

Nuclear Industry: PAUT of coarse grain steel component (2017)

[2] “” Assessment of the reliability of phased array NDT of coarse grain component based on simulation, G. Ribay et al, to be published in the

7th EA reliability workshop proc. (2017)

0 5Defect height (mm)

a90/95 :

1,43 mm

Small fluctuations of cij Large fluctuations of cij

POD curve (Hit/Miss cumulative lognormal)

A90/95 = 17,4 mm

POD curve (Hit/Miss cumulative lognormal)

Defect height (mm)

Pro

babili

tyof dete

ction

(%)

Defect height (mm)

Pro

babili

tyof dete

ction

(%)













MAPOD key idea: To introduce variations of the inputted parameters of the model. The variability of the output of the simulation reproduces the scattering of real NDE results.






MAPOD Group driven by USAF at CNDE (2003-2011)



MAPOD Group, 2003-2011CNDE, USAF,…




Made possible by the computational performances of metamodels


Probabilistic: Active R&D during the last decade on MAPOD, a growing interest in various industries.

New ideas: Thanks to simulation we can try to go beyond the assumptions/limitations of the “standard” (experiment-based) statistical methodology and provide more insight on reliability.









MAPOD Group, 2003-2011CNDE, USAF,…




DOE (intervals)

MMStatistical

Analysis

Offline phaseOnline phase

Variability

MM

PODData base


Probabilistic: Active R&D during the last decade on MAPOD, a growing interest in various industries.


THE USE OF METAMODELS FOR PODS

Computation of 100 000 values (!)for one POD (1-5 s on a PC)

Huge Amount of data

Crack height

Sign

al a

mp

litu

des

New estimation of confidence

REDUCE




Huge Amount of data

Crack height

Sign

al a

mp

litu

des


REDUCE




Huge Amount of data

Crack height

Sign

al a

mp

litu

des


REDUCE




Huge Amount of data

Crack height

Sign

al a

mp

litu

des


REDUCE

Fast simulation of large data-sets makes possible the calculation Calculation of « beams » of POD curves [1].

Every POD curve corresponding to one set of statisticaldistributions.

Estimation of the sensitivity to the inputted statisticaldistributions

[1] Dominguez, N. and al, A new approach of confidence in POD determination using simulation, Rev. of prog in QNDE, VOL

32B, 1749-1756 (2013)




Huge Amount of data

Crack height

Sign

al a

mp

litu

des

Non parametric estimation of POD




Huge Amount of data

Crack height

Sign

al a

mp

litu

des

Non parametric estimation of POD

REDUCE




Huge Amount of data

Crack height

Sign

al a

mp

litu

des

POD + Sensitivity analysis

REDUCE

Help for the design of experiment (POD)

Fill the gap deterministic/probabilistic-> POD + worst case




Huge Amount of data

Crack height

Sign

al a

mp

litu

des


Beam of POD

Beyond the usual hypothesis

REDUCE 2Case 1 REDUCE 3Case 2

Outer Ø = 323 mmThickness = 17.5 mm Outer Ø = 406.2 mm

Thickness = 21.4 mm

Non parametricPOD estimation

Height

Tilt

Thickness

CT Skew

Position

Height

Tilt

POD + Sensitivity analysis

SobolIndex

Assessment of statistical analysis

Fill the gap deterministic/probabilistic

Sensitivity to the variability

REDUCE

MODEL-BASED COMPUTATIONAL TOOLS FOR

NDE RELIABILITY ASSESSMENT:

Challenges & open questions


The principal limitations of MAPOD don’t come only from the use of a model by itselfbut also from the postulated variability inputted in the process.

CHALLENGES & OPEN QUESTIONS

Need to characterize the sometimes complex sources of variability

POD-MAPOD and the real on-site conditions (Human factors)

Reliability of SHM systems

Simulation-based accuracy assessment (sizing)

Need of progress in numerical (physic-based) modelling


Great concern on this issue over the world

From G. Selby, EPRI,ICNDE 2016

FOEHN

From European-American workshop on NDE reliability and BAM works

In France, National funded project launched in 2017

From A. D'AGOSTINO, NRC, 2017

How to include Human factors in NDE Reliability process ?

HUMAN FACTORS, NDE RELIABILITY AND DIGITAL TOOLS



One first idea: Monitoring the inspection to capture gesture variability

Introduction in MAPOD of realistic variabilities(probe position/orientation)

How to make MAPOD estimation closer to on-site conditions?

Skew

Signal amplitudes

Dx Dy

Gironde project: Bayesian inversion



One idea: Monitoring the inspection to capture gesture variability

Introduction in MAPOD of realistic variabilities(probe position/orientation)

Further step: Coupled to real time simulation to carry out POD studies in (more) representative on-site conditions with no need of real mock-ups.

How to make MAPOD estimation closer to on-site conditions?


Injection of the synthetic

signals into the NDT display.

Definition of the scenario

(introduction of defect)

Possibly following a pre-

determined programData from tracking

instrumentation

SIGNAL SYNTHESIS: SIMULATION

NDT signals are synthesized

by real-time simulation.

3D tracking of

transducer position

D. Rodat, F. Guibert, N. Dominguez, and P. Calmon, ‘Operational NDT Simulator, Towards Human Factors Integration in Simulated Probability

Of Detection’, in 43rd Rev. Prog. in QNDE, AIP Conf. Proc. 1806, 140004 (2017).

From D. Rodat’s PhD, Dec. 2018

NDE OPERATIONAL SIMULATION


RELIABILITY OF SHM SYSTEMS

Main specific issues:

Sensors attached to mock-ups: More difficult and expansive experimental trials

Non-independance of data if considering growing defects

Environmental conditions (Temperature, humidity)

Changes in sensor performance over time and possible degradation of sensors

Possibly more sophisticated damage index definition (comparison with pristine, processing of multiple signals, ML, etc..)

General agreement on the importance of simulation (MAPOD) in a methodology whichremains to be established

Structural Health Monitoring

Damage monitoring replaces periodic inspections

Instrumentation of the structure

Network of sensors + decision making systems

Reliability assessment of SHM systems is one major issue for their future deployment

Acting WG on this topic at

See O. Mesnil talk


SIMULATION- BASED ACCURACY ASSESSMENT

Objective: to assess the defect sizing accuracy from a statistical analysis exploring the variability of the influent parameters

To obtain an equivalent of POD for accuracy ?

Even more difficult than POD from experimental study

Simulation and propagation uncertainty as for MAPOD

Steps: - Simulation of the sizing process

- Definition of a “metric” measuring the accuracy


Evaluation of the accuracy of a -6dB drop sizing on TFM inspection of V-weld

Methodology:

Simulation of the sizing procedure

Creation of a metamodel:

- Input: height, depth, tilt and cT

- Output: the sizing error

Statistical analysis: - MC sampling +- estimation of the distribution of errors

Array 7 MHz 64 elts, TFMSteel welded pipe 21 mm, defect: lack of fusion

ILLUSTRATION ON A SCHOOL CASE : TFM INSPECTION

-6dB contour

1. In the « nominal » case (no uncertainty on other parameters)

Range of size: 1-5mm

smallestdefects

0 0.5 1

Pro

bab

ility

den

sity

(u.a

.)

Range of size: 0-5mm

Error (mm)

Good accuracy Average low overestimation No underestimation

2. Accounting for uncertainties(here on the tilt and on the velocity )

0.5 10- 0.5

cT = 3240 mms-1

(true one)

cT = 3340 mms-1

Tilt : [-5 , 5 ]

Loss of accuracy Underestimation possible

Ongoing study

MODEL-BASED COMPUTATIONAL TOOLS

FOR DIAGNOSTICS:

Imaging, Sizing


BACKGROUND: FMC-TFM TECHNIQUES

FMC: Acquisition of the signals for all the pairs of T-R elements

TFM: Processing based on the computation of Time of Flights for all the pixels in the image

Numerous advantages over conventionnal PAUT

Transmit

Receive

•P

Elt n°i Elt n°j

TiP TjP

“Full matrix Capture” TFM: Focusing “everywhere”

Principle

FMC-TFM: Today fast expanding Ultrasonic Array imaging technique

Model-based imaging basedon physical assumptions

Needs for adaptive imaging algorithms to correct the effects of uncertainties/lack of knowledge on the inspected parts


MULTIMODAL TFM

Linear array : 32 elements, 5MHz

Direct imaging of crack-like defects by corner effect

Calibration: 0db SDH Ø2 mm

5 mm

30 mm

20 mm

20 m

m

5 mm

30 mm

20 mm

20 m

m

45°Steel block with notch

H = 5 mm,Tilt = 0°

Steel block with notch H = 5 mm, Tilt = 10°

5 mm

30.4 mm

10°

20 mm

20 m

m

5 mm

30.4 mm

10°

20 mm

20 m

m

TTT

+8dB

LL

SimulationExperiment

-3dB

0dB

LL

-4dB

-3dB

LLT

+1dB


COMPUTATIONAL TOOLS FOR FMC-TFM

Modelling embedded in tools assisting the TFM inspection :

A priori selecting the most relevant images

Correcting of possible artifacts

Providing simulation–assisted diagnostics : Size of the decfect + accuracy/uncertainty

Partially imagedNot imaged Fully imaged

Défaut

verticalDéfaut incliné de

14°

One key idea: Sensitivity maps defined for one orientation of the defect [1]

SEE: Estimation of the weighed number of T-R pairs in condition of specular reflexion

[1] K. Sy, Ph Brédif , E. Iakovleva , D. Lesselier, O. Roy, NDT&E Int., 2018


ADAPTIVE MODEL-BASED TFM IMAGING

ATFM Imaging of cracks: To deal with geometrical uncertainties

(1) Surface image

(2) Back-wall image

Adaptive TFM for crack-type defects

Imaging with half-skip modesImaging with direct paths

Complete the geometry of the part

S. Robert et al, Surface Estimation Methods with Phased-Arrays for Adaptive Ultrasonic Imaging, to be published in Rev. Prog. In Quant. NDE, 34, (2015)


How to correct the effect of a lack of knowledge on the material properties ?

Optimization of the imaging : iterative process using metamodels

Sensitivity to the (unknown) material properties

V-shape weld Cladding (steel)

Stainless steel

Anisotropic weld

37 mm

ROI

Transducer

Isotropic reconstruction

Anisotropic reconstruction

Transducer

ROI

Anisotropic cladding

Ferritic steel 56,5 mm

Anisotropic reconstruction

Isotropic reconstruction

Simulated images




First experimental results on weld mold:Initial image

(isotropic assumption)

Final image

See C. Menard’s talk

8°Weld mold

20 mm

Inconel

20 mm

10 mm

40

mm

10

mm20 mm

Artificial defects: 3 SDH Ø 1.5 mm

Homogeneous structure ( = 8°)

Optimization of 5 input parameters: C11, C33, C13, C55,

+ 9 dB

Isotropic

Optimized


BEYOND IMAGING : INVERSION & AUTOMATIC DIAGNOSTICS

DIAGNOSTIC:

Identification of defects/damaged states : Classification

Defect characterization (location, size): Parametric inversion

Machine learning appears to be a powerful tool in the two cases

Today’s talk: defect characterization (sizing)

ML for flaw characterisation: Learning the inverse model from a « training set » no more than a metamodel


BEYOND IMAGING : INVERSION & AUTOMATIC SIZING

ML for flaw characterisation: Learning the inverse model from a « training set »

Requires a huge amountof representative data in general not available

One attractive solution: Numericaldata to complement/replace experiments in the training phase

In general dimension reduction:From the full signal (image)

extraction of relevant features

Simulation-assisted ML and inversion:

To define the descriptors (features) To select and test the estimator To assess the robustness to uncertainties, …

Illustration on 1st Example : GW Imaging for SHM

Illustration on a 2nd Example : Automatic sizing on a V weld UT inspection


Goal: To develop of a GW SHM solution in order to detect delaminations of the composite face sheets and disbondsbetween face sheets and honeycomb core

Honeycomb sandwich composite.

MACHINE LEARNING FOR AUTOMATIC SIZING

1ST EXAMPLE: PARAMETRIC INVERSION FOR GW SHM

Context : Guided wawes SHM for sandwich composite structure


GUIDED WAVE IMAGING TECHNIQUE

Defect simulated by an attached mass

GWI

30-mm thick honeycomb

Network of PZT sensors paving the structure

Low-frequency GW for honeycomb (~10-40 kHz)

Residual signals (unknown – pristine)

Model-based imaging (DAS, Excitelet,…)

Detection criteria on images

The proposed GW technique

Quite promising results - One stake: Reliability assessment (Work in progress)

Disbonding

Laser doppler velocimetermeasurements


MODEL-BASED IMAGING

Imaging/detection relies on comparison to pristine (reference) signals

Imaging algorithms embedding more or less sophisticated model

RAPID (Reconstruction

Algorithm for Probabilistic Inspection of Damages)

Correlation between pristine and unknown state. No model

DAS (Delay And Sum)

Sommation of residual signals delayed by theoretical times of flight

Excitelet

Correlation between residual signals and theoretical signals at every pixel


MACHINE LEARNING BASED INVERSION

Goal: Automatic sizing (& location) of the defect

Method : Machine learning based inversion

Machine learning

Offline phase: learning the « inverse model » from a numerical data base

• Data: Guided wave images (DAS, Excitelet) of holes

• Dimensionality reduction: PCA

• Regression: SVM, KRR, CNN…

Estimator

( )

Image

Size/location

Numerical database (350 simulated images)Various defect size and position

NB: Simplified case: Aluminium plate





Online phase: Exploitation of the « Inverse metamodel »

Guided wave imageUnknown defect

Predicted size VS true size

Red: Numerical dataGreen : Experimental

Proof of concept:

• Application to numerical data base (test base, 150 images)

• Average absolute error of 0,3mm in sizing

First results on experimental data:

• Excellent prediction (here use of KRR and CNN)

Results






Online phase: Exploitation of the « Inverse metamodel »

Guided wave imageUnknown defect


Red: Numerical dataGreen : Experimental

Proof of concept:

• Application to numerical data base (test base, 150 images)

• Average absolute error of 0,3mm in sizing

First results on experimental data:

• Excellent prediction (here use of KRR and CNN)

Results


AND RETURN TO POD AND RELIABILITY

On the same example (case simplified Al plate): POD estimation

Creation of a numerical data base of images (SFEM code), then a meta-model5mm 7,5mm 10mm 12,5mm 15mm

5mm 7,5mm 10mm 12,5mm 15mm

5mm 7,5mm 10mm 12,5mm 15mm

5mm 7,5mm 10mm 12,5mm 15mm

Introduction of variability

Location of the defect

Temperature: [15,25]°C

Variable measurement noise

Sensors ageing: Variable biasto sensors response (degradation)

Radial Angular

T°

Sensor degradationMeasurement noise

Only proof of concept:+/- arbitrary choices

Estimation of POD (Hit miss)

lin-lin

Logit

a90/95 = 9.9 mm

Influence of sensor degradation

Disabling of sensors: Simulation vs Exp

Nominal 4/8 disabled

Exp Exp

Sim SimNext step: Composites + delamination(ongoing work)


AUTOMATIC SIZING (ITERATIVE INVERSION)

2ND EXAMPLE: UT V-WELD INSPECTION

Inspection on a Ferritic V-weld

Inspected length : 2 mAcquisition : 5-10 mnAnalysis by te operator : ~1h

Objective: to propose a procedure of automatic sizing of breaking internal defect

French national funded project

Probe : Linear PA, 5Mhz, 16 elementsAcquisition scheme : Sectorial scanning

Simulation has been used to:

Generate a data base

To determine a descriptor relevant for the inversion

To evaluate the accuracy of the sizing

To evaluate the robustness to irregular crack-profile




Definition of the sizing descriptor (the feature on which will be applied the inversion):

Chosen from the study of numerical signals:

Derived from the distribution of the times of flights of samples exeeding an amplitude threshhold (all shots of one sectorial scan).

Independant to amplitude : no calibration sim/exp needed !

Creation of a metamodel:

Inputs

Metamodel validation

Models Errors

Inputs:- Defect height - Defect tilt- Probe distance to weld- Thickness of the part

Output: The sizing descriptor




Sensitivity analysis of the sizing descriptor

- Confirms the strong dependancy to the Height

- Estimates the sensitivity to other parameters(robustness to uncertainty)

- Expected: less accurate for largest defect

Smallest defects (1-5mm)

Largest defects (10-15mm)

Height

Height

Height

Tilt

PositioningThickness

Tilt

Positioning

Thickness

Sobol indexes

Sobol indexes

Sobol indexes




First experimental validation:

Scanning n 1 with good parallelism

CSCAN CSCAN

Scanning n 2 with intentional disorientation (uncertainty of probe positioning)

mm

1,5

2

3

4

5

7,5

10

12,5

15

Demonstration on a nominal case: set of notches




Ongoing work

Estimation of the sensitivity to irregular profiles

+ Hybrid Ray-based/FEM model

Creation of parametric set of profiles (new CIVA capability)

Numerical data base

Integration of the influence of the profile variability on the accuracy of the sizing

Estimated accuracy :+/- 1 mm for largest defects

SUMMARY


SUMMARY

An always more central role of modelling for fulfilling the challenges of NDE&SHM

Physic-based models remain the socle, but more and more included in meta-model strategy their capabilities are considerably enhanced.

The use of meta-models giving access to quasi-unlimited amount of numerical data opens the way to new ideas for POD estimation and statistical studies.

Models are embedded in ultrasonic imaging algorithms which tend to become more and more adaptive.

Models can be used to feed automatic diagnostic based on iterative inversion or machine learning. One double challenge: the representativity of numerical data and the robustness of ML.

Continuous integration of the “worthy” developments in the platform CIVA.”

Commissariat à l’énergie atomique et aux énergies alternativesInstitut List | CEA SACLAY NANO-INNOV | BAT. 861 – PC14291191 Gif-sur-Yvette Cedex - FRANCEwww-list.cea.fr

Établissement public à caractère industriel et commercial | RCS Paris B 775 685 019

Thank you for your attention Principal contributors to this talk:

X. Artusi, S. Leberre

R. Miorelli, D. Rodat

T. Druet, A. Kulakovskyi

B. Chapuis, C. Reboud

C. Ménard, S. Robert

Documents

MODEL-BASED COMPUTATIONAL TOOLS FOR IMAGING, …